Text-to-Video
Diffusers
LTX2VideoPipeline
custom_ltx2
video-generation
image-to-video
audio-to-video
quantized
4-bit precision
int4
svdquant
ltx-2
ltx-2.5
quantfunc
Instructions to use QuantFunc/LTX-2.5-QuantFunc-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuantFunc/LTX-2.5-QuantFunc-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QuantFunc/LTX-2.5-QuantFunc-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 424 Bytes
1a837a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"model_type": "custom_ltx2",
"architectures": [
"LTX2VideoPipeline"
],
"name_or_path": "QuantFunc/LTX-2.5-QuantFunc-4bit",
"task": "text-to-video",
"framework": "quantfunc",
"license": "other",
"description": "Pre-quantized LTX-2.5 22B distilled video+audio model by QuantFunc",
"_comment": "This is a minimal config to enable download stats on HF Hub. Not used for loading."
}
|